This thesis examines the cross-sectional dynamic performance of the US stock markets through Principal Components analysis (PCA). We examine the annual and semi-annual performance, from 1928 to 2015, of the portfolios obtained from the top five principal components from past returns. These capture seventy percent of the variation in assets returns. The first principal component has over ninety percent annual correlation with the market. The second, third, fourth and fifth principal components have persistent characteristics different from the market. A Sharpe Ratio of 0.79 and maximum drawdown of 0.27 could be
obtained by investing in a combination of the principal portfolios, compared to a Sharpe ratio of 0.40 for the market and a maximum drawdown of 0.80. The four-factors (market,
size, value and momentum) and betting against Beta regressions show a significantly positive alpha, whence portfolio performance cannot be explained by these factors. Importantly, the composite portfolio...

Translation is a task challenging enough, and legal translation is a task that is even more so. This is because not only is human language unique, but the law is also unique in that it reflects the culture of the time and place. Just as languages are grouped into families that originate from a particular protolanguage, so are laws, or more specifically jurisdiction-based national bodies of law. As a result, several legal traditions have evolved around the world, each of them viewing the “legal world” in a very different way through very different “law lenses”, with each of them being expressed in a different human language - most likely an official language of the jurisdiction. In one word, there is probably no uniform way of approaching and understanding the laws and norms of every society. Against this backdrop, it would not take too much imagination for one to understand how difficult and problematic legal translation, the process of expressing a piece of legislation or witness (...

In this article we describe the use of a multi-objective evolutionary algorithm for portfolio optimisation based on historical data for the S&P 500. Portfolio optimisation seeks to identify manageable investments that provide a high expected return with relatively low risk. We developed a set of metrics for qualifying the risk/return characteristics of a portfolio's historical performance and combined this with an island model genetic algorithm to identify optimised portfolios. The algorithm was successful in selecting investment strategies with high returns and relatively low volatility. However, although these solutions performed well on historical data, they were not predictive of future returns, with optimised portfolios failing to perform above chance. The implications of these findings are discussed.